PlantSynBio: Deciphering the grammar of crop regulatory DNA for precise engineering of gene expression

植物研究人员和育种者一致认为，需要技术进步来满足未来对农产品的需求，特别是在气候危机加速的情况下。虽然传统的植物育种和施肥在过去使农业产量大幅增加，但近年来产量已趋于稳定。该项目使用新的基因组规模技术和先进的分析方法来识别和表征驱动玉米和番茄生长和环境适应力的数万个遗传元件，这两种作物代表了两大类植物。所得到的数据集的大尺寸，使这些元素的系统计算分析，并允许设计的合成元素所需的功能。这些设计对未来作物工程的适用性和安全性将在实验室的玉米和番茄植物中进行研究。今天的植物研究人员和育种者需要熟练地进行基因组规模的实验和应用先进的计算方法。该项目将为几名本科生和博士后研究员的培训和职业发展提供资金。对植物基因调控的有限理解和缺乏多样的、植物衍生的调控元件来驱动转基因，阻碍了植物合成生物学。为了促进植物合成生物学，该项目将应用大规模并行报告分析和机器学习来破译玉米和番茄中基因调控元件的语法，包括增强子、启动子、绝缘子和终止子，以及它们在四种常见环境条件下的局部相互作用。这些调控元件和元件组合的显著特征将通过卷积神经网络模型来识别，该模型将用于进化和设计具有所需特征的合成元件。设计的启动子和增强子元件，沿着植物衍生的绝缘子和终止子，将作为合成生物学应用的构建模块。模型推断将在转基因作物与工程天然调控元件，并通过整合多基因报告盒，其可调和可编程的活动是由合成的调控元件驱动进行测试。该项目的成果将推动植物基因调控和作物合成生物学超越现有的知识和工具，和启动作物工程的努力。这个奖项是共同的-该奖项由整合有机体系统部的植物基因组研究计划和分子与细胞生物科学部的系统与合成生物学集群资助。该奖项反映了NSF的法定使命，并被认为值得通过以下方式获得支持：使用基金会的知识价值和更广泛的影响审查标准进行评估。

Plant researchers and breeders agree that technological advances are needed to meet the future demands for agricultural products, especially in light of the accelerating climate crisis. Although traditional plant breeding and fertilizer application have produced large increases in agricultural yields in the past, yields have plateaued in recent years. This project uses novel genome-scale technology and advanced analysis methods to identity and characterize the tens of thousands of genetic elements that drive growth and environmental resilience in maize and tomato, crops that represent two major classes of plants. The large size of the resulting data sets enables systematic computational analyses of these elements and allows for design of synthetic elements with desirable features. The applicability and safety of these designs for future crop engineering will be examined in maize and tomato plants in the laboratory. Today’s plant researchers and breeders need to be fluent in conducting genome-scale experiments and applying advanced computational methods. This project will provide funds for the training and the career development of several undergraduate students and postdoctoral fellows.The limited understanding of plant gene regulation and lack of diverse, plant-derived regulatory elements to drive transgenes have hampered plant synthetic biology. To facilitate plant synthetic biology, this project will apply massively parallel reporter assays and machine learning to decipher the grammar of gene-regulatory elements in maize and tomato, including enhancers, promoters, insulators and terminators, and their local interactions in response to four common environmental conditions. The salient features of these regulatory elements and element combinations will be identified by convolutional neural network models, which will be used to evolve and design synthetic elements with desired features. Designed promoter and enhancer elements, along with plant-derived insulators and terminators, will serve as building blocks for synthetic biology applications. Model inferences will be tested in transgenic crops with engineered native regulatory elements and through the integration of multi-gene reporter cassettes whose tunable and programmable activity is driven by synthetic regulatory elements. This project’s results will propel plant gene regulation and crop synthetic biology beyond current knowledge and tools, and jumpstart crop engineering efforts.This award was co-funded by the Plant Genome Research Program in the Division of Integrative Organismal Systems and the Systems and Synthetic Biology cluster in the Division of Molecular and Cellular Biosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.